PhD Thesis.Shape optimisation is widely used in industry to improve the performance of
the product. When performing aerodynamic analysis with CFD (Computational
Fluid Dynamics), gradient-based optimisation methods are normally preferred if
the number of design variables is high. These methods require the evaluation of
the total derivatives, which can be split into two terms: the flow and the shape
derivatives.
While evaluating the flow derivatives with the adjoint CFD method, this thesis
demonstrates that the shape derivatives can be calculated with algorithmically
differentiated parametric CAD models. The development of such CAD models
allows to compute the derivatives exactly and, by utilising the reverse mode variant
of algorithmic differentiation, independently of the number of design parameters.
This makes the computation of the shape derivatives efficient and robust.
The parametrisation of the test-cases (a cooling channel and a compressor stator
blade) is defined by intuitive and designer-friendly variables which capture the
shape modes which mainly affect the objective function.
The optimised parametric CAD models are compared to reference results. These
results are set as the optimal shapes given by parametrisations with refined design
space. The reference results of the cooling channel are identified in the literature.
For the blade test-case, the design space of the parametric-based CAD model is
enlarged (almost quadrupled). The optimised shape obtained with the parametricbased
design is able to reproduce the same design modes provided by the enlarged
design space.
The fit of the assembly constraints of the blade’s test-case (four mounting bolts)
during the flow optimisation has never been demonstrated. This is due to the
arduous identification of a differentiable assembly constraints’ function. This thesis
demonstrates that an approach based on the detection of a signed distance
between the blade and the bolts succeeds in fitting the assembly constraints.
